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589 lines
22 KiB
Python
589 lines
22 KiB
Python
7 years ago
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#==========================================================#
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#==========================================================#
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# Extract shorelines from Landsat images
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#==========================================================#
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#==========================================================#
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#==========================================================#
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# Initial settings
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#==========================================================#
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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import ee
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import pdb
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# other modules
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from osgeo import gdal, ogr, osr
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import pickle
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import matplotlib.cm as cm
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from pylab import ginput
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from shapely.geometry import LineString
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# image processing modules
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import skimage.filters as filters
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import skimage.exposure as exposure
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import skimage.transform as transform
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import sklearn.decomposition as decomposition
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import skimage.measure as measure
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import skimage.morphology as morphology
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# machine learning modules
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from sklearn.model_selection import train_test_split
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from sklearn.neural_network import MLPClassifier
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from sklearn.preprocessing import StandardScaler, Normalizer
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from sklearn.externals import joblib
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# import own modules
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import functions.utils as utils
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import functions.sds as sds
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# some other settings
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np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
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plt.rcParams['axes.grid'] = True
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plt.rcParams['figure.max_open_warning'] = 100
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ee.Initialize()
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#==========================================================#
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# Parameters
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#==========================================================#
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sitename = 'NARRA'
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cloud_thresh = 0.7 # threshold for cloud cover
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plot_bool = False # if you want the plots
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output_epsg = 28356 # GDA94 / MGA Zone 56
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buffer_size = 7 # radius (in pixels) of disk for buffer (pixel classification)
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min_beach_size = 20 # number of pixels in a beach (pixel classification)
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dist_ref = 100 # maximum distance from reference point
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min_length_wl = 200 # minimum length of shoreline LineString to be kept
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manual_bool = True # to manually check images
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output = dict([])
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#==========================================================#
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# Metadata
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#==========================================================#
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filepath = os.path.join(os.getcwd(), 'data', sitename)
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with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'rb') as f:
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metadata = pickle.load(f)
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#%%
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#==========================================================#
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# Read S2 images
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#==========================================================#
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satname = 'S2'
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dates = metadata[satname]['dates']
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input_epsg = 32756 # metadata[satname]['epsg']
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# path to images
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filepath10 = os.path.join(os.getcwd(), 'data', sitename, satname, '10m')
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filenames10 = os.listdir(filepath10)
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filepath20 = os.path.join(os.getcwd(), 'data', sitename, satname, '20m')
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filenames20 = os.listdir(filepath20)
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filepath60 = os.path.join(os.getcwd(), 'data', sitename, satname, '60m')
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filenames60 = os.listdir(filepath60)
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if (not len(filenames10) == len(filenames20)) or (not len(filenames20) == len(filenames60)):
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raise 'error: not the same amount of files for 10, 20 and 60 m'
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N = len(filenames10)
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# initialise variables
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cloud_cover_ts = []
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acc_georef_ts = []
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date_acquired_ts = []
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filename_ts = []
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satname_ts = []
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timestamp = []
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shorelines = []
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idx_skipped = []
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spacing = '=========================================================='
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msg = ' %s\n %s\n %s' % (spacing, satname, spacing)
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print(msg)
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for i in range(N):
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# read 10m bands
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fn = os.path.join(filepath10, filenames10[i])
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data = gdal.Open(fn, gdal.GA_ReadOnly)
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georef = np.array(data.GetGeoTransform())
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bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
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im10 = np.stack(bands, 2)
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im10 = im10/10000 # TOA scaled to 10000
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# if image is only zeros, skip it
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if sum(sum(sum(im10))) < 1:
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print('skip ' + str(i) + ' - no data')
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idx_skipped.append(i)
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continue
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nrows = im10.shape[0]
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ncols = im10.shape[1]
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# read 20m band (SWIR1)
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fn = os.path.join(filepath20, filenames20[i])
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data = gdal.Open(fn, gdal.GA_ReadOnly)
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bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
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im20 = np.stack(bands, 2)
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im20 = im20[:,:,0]
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im20 = im20/10000 # TOA scaled to 10000
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im_swir = transform.resize(im20, (nrows, ncols), order=1, preserve_range=True, mode='constant')
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im_swir = np.expand_dims(im_swir, axis=2)
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# append down-sampled swir band to the 10m bands
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im_ms = np.append(im10, im_swir, axis=2)
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# read 60m band (QA)
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fn = os.path.join(filepath60, filenames60[i])
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data = gdal.Open(fn, gdal.GA_ReadOnly)
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bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
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im60 = np.stack(bands, 2)
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im_qa = im60[:,:,0]
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cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
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cloud_mask = transform.resize(cloud_mask,(nrows, ncols), order=0, preserve_range=True, mode='constant')
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# check if -inf or nan values on any band and add to cloud mask
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for k in range(im_ms.shape[2]):
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im_inf = np.isin(im_ms[:,:,k], -np.inf)
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im_nan = np.isnan(im_ms[:,:,k])
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cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
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# calculate cloud cover and if above threshold, skip it
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cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
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if cloud_cover > cloud_thresh:
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print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)')
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idx_skipped.append(i)
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continue
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# rescale image intensity for display purposes
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im_display = sds.rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9, False)
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# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
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im_classif, im_labels = sds.classify_image_NN_nopan(im_ms, cloud_mask, min_beach_size, plot_bool)
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# if there aren't any sandy pixels
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if sum(sum(im_labels[:,:,0])) == 0 :
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# use global threshold
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im_ndwi = sds.nd_index(im_ms[:,:,4], im_ms[:,:,1], cloud_mask, plot_bool)
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contours = sds.find_wl_contours(im_ndwi, cloud_mask, plot_bool)
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else:
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# use specific threhsold
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contours_wi, contours_mwi = sds.find_wl_contours2(im_ms, im_labels, cloud_mask, buffer_size, plot_bool)
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# convert from pixels to world coordinates
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wl_coords = sds.convert_pix2world(contours_mwi, georef)
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# convert to output epsg spatial reference
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wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg)
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# remove contour lines that have a perimeter < min_length_wl
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wl_good = []
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for l, wls in enumerate(wl):
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coords = [(wls[k,0], wls[k,1]) for k in range(len(wls))]
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a = LineString(coords) # shapely LineString structure
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if a.length >= min_length_wl:
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wl_good.append(wls)
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# format points and only select the ones close to the refpoints
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x_points = np.array([])
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y_points = np.array([])
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for k in range(len(wl_good)):
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x_points = np.append(x_points,wl_good[k][:,0])
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y_points = np.append(y_points,wl_good[k][:,1])
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wl_good = np.transpose(np.array([x_points,y_points]))
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temp = np.zeros((len(wl_good))).astype(bool)
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for k in range(len(refpoints)):
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temp = np.logical_or(np.linalg.norm(wl_good - refpoints[k,[0,1]], axis=1) < dist_ref, temp)
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wl_final = wl_good[temp]
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# plot output
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plt.figure()
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im = np.copy(im_display)
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colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
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for k in range(0,im_labels.shape[2]):
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im[im_labels[:,:,k],0] = colours[k,0]
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im[im_labels[:,:,k],1] = colours[k,1]
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im[im_labels[:,:,k],2] = colours[k,2]
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plt.imshow(im)
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for k,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
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plt.title(satname + ' ' + metadata[satname]['dates'][i].strftime('%Y-%m-%d') + ' acc : ' + str(metadata[satname]['acc_georef'][i]) + ' m' )
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plt.draw()
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pt_in = np.array(ginput(n=1, timeout=1000))
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plt.close()
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# if image is rejected, skip it
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if pt_in[0][1] > nrows/2:
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print('skip ' + str(i) + ' - rejected')
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idx_skipped.append(i)
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continue
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# if accepted, store the data
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cloud_cover_ts.append(cloud_cover)
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acc_georef_ts.append(metadata[satname]['acc_georef'][i])
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filename_ts.append(filenames10[i])
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satname_ts.append(satname)
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date_acquired_ts.append(filenames10[i][:10])
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timestamp.append(metadata[satname]['dates'][i])
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shorelines.append(wl_final)
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# store in output structure
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output[satname] = {'dates':timestamp, 'shorelines':shorelines, 'idx_skipped':idx_skipped,
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'metadata':{'filenames':filename_ts, 'satname':satname_ts, 'cloud_cover':cloud_cover_ts,
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'acc_georef':acc_georef_ts}}
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del idx_skipped
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#%%
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#==========================================================#
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# Read L7&L8 images
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#==========================================================#
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satname = 'L8'
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dates = metadata[satname]['dates']
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input_epsg = 32656 # metadata[satname]['epsg']
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# path to images
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filepath_pan = os.path.join(os.getcwd(), 'data', sitename, 'L7&L8', 'pan')
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filepath_ms = os.path.join(os.getcwd(), 'data', sitename, 'L7&L8', 'ms')
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filenames_pan = os.listdir(filepath_pan)
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filenames_ms = os.listdir(filepath_ms)
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if (not len(filenames_pan) == len(filenames_ms)):
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raise 'error: not the same amount of files for pan and ms'
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N = len(filenames_pan)
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# initialise variables
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cloud_cover_ts = []
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acc_georef_ts = []
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date_acquired_ts = []
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filename_ts = []
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satname_ts = []
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timestamp = []
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shorelines = []
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idx_skipped = []
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spacing = '=========================================================='
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msg = ' %s\n %s\n %s' % (spacing, satname, spacing)
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print(msg)
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for i in range(N):
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# get satellite name
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sat = filenames_pan[i][20:22]
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# read pan image
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fn_pan = os.path.join(filepath_pan, filenames_pan[i])
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data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
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georef = np.array(data.GetGeoTransform())
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bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
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im_pan = np.stack(bands, 2)[:,:,0]
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nrows = im_pan.shape[0]
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ncols = im_pan.shape[1]
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# read ms image
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fn_ms = os.path.join(filepath_ms, filenames_ms[i])
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data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
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bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
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im_ms = np.stack(bands, 2)
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# cloud mask
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im_qa = im_ms[:,:,5]
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cloud_mask = sds.create_cloud_mask(im_qa, sat, plot_bool)
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cloud_mask = transform.resize(cloud_mask, (nrows, ncols), order=0, preserve_range=True, mode='constant').astype('bool_')
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# resize the image using bilinear interpolation (order 1)
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im_ms = im_ms[:,:,:5]
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im_ms = transform.resize(im_ms,(nrows, ncols), order=1, preserve_range=True, mode='constant')
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# check if -inf or nan values on any band and add to cloud mask
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for k in range(im_ms.shape[2]+1):
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if k == 5:
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im_inf = np.isin(im_pan, -np.inf)
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im_nan = np.isnan(im_pan)
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else:
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im_inf = np.isin(im_ms[:,:,k], -np.inf)
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im_nan = np.isnan(im_ms[:,:,k])
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cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
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# calculate cloud cover and skip image if above threshold
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cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
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if cloud_cover > cloud_thresh:
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print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)')
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idx_skipped.append(i)
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continue
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# Pansharpen image (different for L8 and L7)
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if sat == 'L7':
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# pansharpen (Green, Red, NIR) and downsample Blue and SWIR1
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im_ms_ps = sds.pansharpen(im_ms[:,:,[1,2,3]], im_pan, cloud_mask, plot_bool)
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im_ms_ps = np.append(im_ms[:,:,[0]], im_ms_ps, axis=2)
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im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[4]], axis=2)
|
||
|
|
im_display = sds.rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9, False)
|
||
|
|
elif sat == 'L8':
|
||
|
|
# pansharpen RGB image and downsample NIR and SWIR1
|
||
|
|
im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool)
|
||
|
|
im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2)
|
||
|
|
im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 99.9, False)
|
||
|
|
|
||
|
|
# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
|
||
|
|
im_classif, im_labels = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size, plot_bool)
|
||
|
|
|
||
|
|
# if there aren't any sandy pixels
|
||
|
|
if sum(sum(im_labels[:,:,0])) == 0 :
|
||
|
|
# use global threshold
|
||
|
|
im_ndwi = sds.nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, plot_bool)
|
||
|
|
contours = sds.find_wl_contours(im_ndwi, cloud_mask, plot_bool)
|
||
|
|
else:
|
||
|
|
# use specific threhsold
|
||
|
|
contours_wi, contours_mwi = sds.find_wl_contours2(im_ms_ps, im_labels, cloud_mask, buffer_size, plot_bool)
|
||
|
|
|
||
|
|
# convert from pixels to world coordinates
|
||
|
|
wl_coords = sds.convert_pix2world(contours_mwi, georef)
|
||
|
|
# convert to output epsg spatial reference
|
||
|
|
wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg)
|
||
|
|
|
||
|
|
# remove contour lines that have a perimeter < min_length_wl
|
||
|
|
wl_good = []
|
||
|
|
for l, wls in enumerate(wl):
|
||
|
|
coords = [(wls[k,0], wls[k,1]) for k in range(len(wls))]
|
||
|
|
a = LineString(coords) # shapely LineString structure
|
||
|
|
if a.length >= min_length_wl:
|
||
|
|
wl_good.append(wls)
|
||
|
|
|
||
|
|
# format points and only select the ones close to the refpoints
|
||
|
|
x_points = np.array([])
|
||
|
|
y_points = np.array([])
|
||
|
|
for k in range(len(wl_good)):
|
||
|
|
x_points = np.append(x_points,wl_good[k][:,0])
|
||
|
|
y_points = np.append(y_points,wl_good[k][:,1])
|
||
|
|
wl_good = np.transpose(np.array([x_points,y_points]))
|
||
|
|
temp = np.zeros((len(wl_good))).astype(bool)
|
||
|
|
for k in range(len(refpoints)):
|
||
|
|
temp = np.logical_or(np.linalg.norm(wl_good - refpoints[k,[0,1]], axis=1) < dist_ref, temp)
|
||
|
|
wl_final = wl_good[temp]
|
||
|
|
|
||
|
|
# plot output
|
||
|
|
plt.figure()
|
||
|
|
plt.subplot(121)
|
||
|
|
im = np.copy(im_display)
|
||
|
|
colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
|
||
|
|
for k in range(0,im_labels.shape[2]):
|
||
|
|
im[im_labels[:,:,k],0] = colours[k,0]
|
||
|
|
im[im_labels[:,:,k],1] = colours[k,1]
|
||
|
|
im[im_labels[:,:,k],2] = colours[k,2]
|
||
|
|
plt.imshow(im)
|
||
|
|
for k,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
|
||
|
|
plt.title(sat + ' ' + metadata[satname]['dates'][i].strftime('%Y-%m-%d') + ' acc : ' + str(metadata[satname]['acc_georef'][i]) + ' m' )
|
||
|
|
|
||
|
|
pt_in = np.array(ginput(n=1, timeout=1000))
|
||
|
|
plt.close()
|
||
|
|
|
||
|
|
# if image is rejected, skip it
|
||
|
|
if pt_in[0][1] > nrows/2:
|
||
|
|
print('skip ' + str(i) + ' - rejected')
|
||
|
|
idx_skipped.append(i)
|
||
|
|
continue
|
||
|
|
|
||
|
|
# if accepted, store the data
|
||
|
|
cloud_cover_ts.append(cloud_cover)
|
||
|
|
acc_georef_ts.append(metadata[satname]['acc_georef'][i])
|
||
|
|
|
||
|
|
filename_ts.append(filenames_pan[i])
|
||
|
|
satname_ts.append(sat)
|
||
|
|
date_acquired_ts.append(filenames_pan[i][:10])
|
||
|
|
|
||
|
|
timestamp.append(metadata[satname]['dates'][i])
|
||
|
|
shorelines.append(wl_final)
|
||
|
|
|
||
|
|
# store in output structure
|
||
|
|
output[satname] = {'dates':timestamp, 'shorelines':shorelines, 'idx_skipped':idx_skipped,
|
||
|
|
'metadata':{'filenames':filename_ts, 'satname':satname_ts, 'cloud_cover':cloud_cover_ts,
|
||
|
|
'acc_georef':acc_georef_ts}}
|
||
|
|
|
||
|
|
del idx_skipped
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
#%%
|
||
|
|
#==========================================================#
|
||
|
|
# Read L5 images
|
||
|
|
#==========================================================#
|
||
|
|
|
||
|
|
satname = 'L5'
|
||
|
|
dates = metadata[satname]['dates']
|
||
|
|
input_epsg = 32656 # metadata[satname]['epsg']
|
||
|
|
|
||
|
|
# path to images
|
||
|
|
filepath_img = os.path.join(os.getcwd(), 'data', sitename, satname, '30m')
|
||
|
|
filenames = os.listdir(filepath_img)
|
||
|
|
N = len(filenames)
|
||
|
|
|
||
|
|
# initialise variables
|
||
|
|
cloud_cover_ts = []
|
||
|
|
acc_georef_ts = []
|
||
|
|
date_acquired_ts = []
|
||
|
|
filename_ts = []
|
||
|
|
satname_ts = []
|
||
|
|
timestamp = []
|
||
|
|
shorelines = []
|
||
|
|
idx_skipped = []
|
||
|
|
|
||
|
|
|
||
|
|
spacing = '=========================================================='
|
||
|
|
msg = ' %s\n %s\n %s' % (spacing, satname, spacing)
|
||
|
|
print(msg)
|
||
|
|
|
||
|
|
for i in range(N):
|
||
|
|
|
||
|
|
# read ms image
|
||
|
|
fn = os.path.join(filepath_img, filenames[i])
|
||
|
|
data = gdal.Open(fn, gdal.GA_ReadOnly)
|
||
|
|
georef = np.array(data.GetGeoTransform())
|
||
|
|
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
|
||
|
|
im_ms = np.stack(bands, 2)
|
||
|
|
|
||
|
|
# down-sample to half hte original pixel size
|
||
|
|
nrows = im_ms.shape[0]*2
|
||
|
|
ncols = im_ms.shape[1]*2
|
||
|
|
|
||
|
|
# cloud mask
|
||
|
|
im_qa = im_ms[:,:,5]
|
||
|
|
im_ms = im_ms[:,:,:-1]
|
||
|
|
cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
|
||
|
|
cloud_mask = transform.resize(cloud_mask, (nrows, ncols), order=0, preserve_range=True, mode='constant').astype('bool_')
|
||
|
|
|
||
|
|
# resize the image using bilinear interpolation (order 1)
|
||
|
|
im_ms = transform.resize(im_ms,(nrows, ncols), order=1, preserve_range=True, mode='constant')
|
||
|
|
|
||
|
|
# adjust georef vector (scale becomes 15m and origin is adjusted to the center of new corner pixel)
|
||
|
|
georef[1] = 15
|
||
|
|
georef[5] = -15
|
||
|
|
georef[0] = georef[0] + 7.5
|
||
|
|
georef[3] = georef[3] - 7.5
|
||
|
|
|
||
|
|
# check if -inf or nan values on any band and add to cloud mask
|
||
|
|
for k in range(im_ms.shape[2]):
|
||
|
|
im_inf = np.isin(im_ms[:,:,k], -np.inf)
|
||
|
|
im_nan = np.isnan(im_ms[:,:,k])
|
||
|
|
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
|
||
|
|
|
||
|
|
# calculate cloud cover and skip image if above threshold
|
||
|
|
cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
|
||
|
|
if cloud_cover > cloud_thresh:
|
||
|
|
print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)')
|
||
|
|
idx_skipped.append(i)
|
||
|
|
continue
|
||
|
|
|
||
|
|
# rescale image intensity for display purposes
|
||
|
|
im_display = sds.rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9, False)
|
||
|
|
|
||
|
|
# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
|
||
|
|
im_classif, im_labels = sds.classify_image_NN_nopan(im_ms, cloud_mask, min_beach_size, plot_bool)
|
||
|
|
|
||
|
|
# if there aren't any sandy pixels
|
||
|
|
if sum(sum(im_labels[:,:,0])) == 0 :
|
||
|
|
# use global threshold
|
||
|
|
im_ndwi = sds.nd_index(im_ms[:,:,4], im_ms[:,:,1], cloud_mask, plot_bool)
|
||
|
|
contours = sds.find_wl_contours(im_ndwi, cloud_mask, plot_bool)
|
||
|
|
else:
|
||
|
|
# use specific threhsold
|
||
|
|
contours_wi, contours_mwi = sds.find_wl_contours2(im_ms, im_labels, cloud_mask, buffer_size, plot_bool)
|
||
|
|
|
||
|
|
# convert from pixels to world coordinates
|
||
|
|
wl_coords = sds.convert_pix2world(contours_mwi, georef)
|
||
|
|
# convert to output epsg spatial reference
|
||
|
|
wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg)
|
||
|
|
|
||
|
|
# remove contour lines that have a perimeter < min_length_wl
|
||
|
|
wl_good = []
|
||
|
|
for l, wls in enumerate(wl):
|
||
|
|
coords = [(wls[k,0], wls[k,1]) for k in range(len(wls))]
|
||
|
|
a = LineString(coords) # shapely LineString structure
|
||
|
|
if a.length >= min_length_wl:
|
||
|
|
wl_good.append(wls)
|
||
|
|
|
||
|
|
# format points and only select the ones close to the refpoints
|
||
|
|
x_points = np.array([])
|
||
|
|
y_points = np.array([])
|
||
|
|
for k in range(len(wl_good)):
|
||
|
|
x_points = np.append(x_points,wl_good[k][:,0])
|
||
|
|
y_points = np.append(y_points,wl_good[k][:,1])
|
||
|
|
wl_good = np.transpose(np.array([x_points,y_points]))
|
||
|
|
temp = np.zeros((len(wl_good))).astype(bool)
|
||
|
|
for k in range(len(refpoints)):
|
||
|
|
temp = np.logical_or(np.linalg.norm(wl_good - refpoints[k,[0,1]], axis=1) < dist_ref, temp)
|
||
|
|
wl_final = wl_good[temp]
|
||
|
|
|
||
|
|
# plot output
|
||
|
|
plt.figure()
|
||
|
|
plt.subplot(121)
|
||
|
|
im = np.copy(im_display)
|
||
|
|
colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
|
||
|
|
for k in range(0,im_labels.shape[2]):
|
||
|
|
im[im_labels[:,:,k],0] = colours[k,0]
|
||
|
|
im[im_labels[:,:,k],1] = colours[k,1]
|
||
|
|
im[im_labels[:,:,k],2] = colours[k,2]
|
||
|
|
plt.imshow(im)
|
||
|
|
for k,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
|
||
|
|
plt.title(satname + ' ' + metadata[satname]['dates'][i].strftime('%Y-%m-%d') + ' acc : ' + str(metadata[satname]['acc_georef'][i]) + ' m' )
|
||
|
|
plt.subplot(122)
|
||
|
|
plt.axis('equal')
|
||
|
|
plt.axis('off')
|
||
|
|
plt.plot(refpoints[:,0], refpoints[:,1], 'k.')
|
||
|
|
plt.plot(wl_final[:,0], wl_final[:,1], 'r.')
|
||
|
|
mng = plt.get_current_fig_manager()
|
||
|
|
mng.window.showMaximized()
|
||
|
|
plt.tight_layout()
|
||
|
|
plt.draw()
|
||
|
|
|
||
|
|
pt_in = np.array(ginput(n=1, timeout=1000))
|
||
|
|
plt.close()
|
||
|
|
|
||
|
|
# if image is rejected, skip it
|
||
|
|
if pt_in[0][1] > nrows/2:
|
||
|
|
print('skip ' + str(i) + ' - rejected')
|
||
|
|
idx_skipped.append(i)
|
||
|
|
continue
|
||
|
|
|
||
|
|
# if accepted, store the data
|
||
|
|
cloud_cover_ts.append(cloud_cover)
|
||
|
|
acc_georef_ts.append(metadata[satname]['acc_georef'][i])
|
||
|
|
|
||
|
|
filename_ts.append(filenames[i])
|
||
|
|
satname_ts.append(satname)
|
||
|
|
date_acquired_ts.append(filenames[i][:10])
|
||
|
|
|
||
|
|
timestamp.append(metadata[satname]['dates'][i])
|
||
|
|
shorelines.append(wl_final)
|
||
|
|
|
||
|
|
# store in output structure
|
||
|
|
output[satname] = {'dates':timestamp, 'shorelines':shorelines, 'idx_skipped':idx_skipped,
|
||
|
|
'metadata':{'filenames':filename_ts, 'satname':satname_ts, 'cloud_cover':cloud_cover_ts,
|
||
|
|
'acc_georef':acc_georef_ts}}
|
||
|
|
|
||
|
|
del idx_skipped
|
||
|
|
|
||
|
|
#==========================================================#
|
||
|
|
#==========================================================#
|
||
|
|
#==========================================================#
|
||
|
|
#==========================================================#
|
||
|
|
|
||
|
|
#%%
|
||
|
|
# save output
|
||
|
|
with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'wb') as f:
|
||
|
|
pickle.dump(output, f)
|
||
|
|
|
||
|
|
# save idx_skipped
|
||
|
|
#idx_skipped = dict([])
|
||
|
|
#for satname in list(output.keys()):
|
||
|
|
# idx_skipped[satname] = output[satname]['idx_skipped']
|
||
|
|
#with open(os.path.join(filepath, sitename + '_idxskipped' + '.pkl'), 'wb') as f:
|
||
|
|
# pickle.dump(idx_skipped, f)
|
||
|
|
|